Abstract: The primate visual system has inspired the development of deep artificial
neural networks, which have revolutionized the computer vision domain. Yet
these networks are much less energy-efficient than their biological
counterparts, and they are typically trained with backpropagation, which is
extremely data-hungry. To address these limitations, we used a deep
convolutional spiking neural network (DCSNN) and a latency-coding scheme. We
trained it using a combination of spike-timing-dependent plasticity (STDP) for
the lower layers and reward-modulated STDP (R-STDP) for the higher ones. In
short, with R-STDP a correct (resp. incorrect) decision leads to STDP (resp.
anti-STDP). This approach led to an accuracy of $97.2\%$ on MNIST, without
requiring an external classifier. In addition, we demonstrated that R-STDP
extracts features that are diagnostic for the task at hand, and discards the
other ones, whereas STDP extracts any feature that repeats. Finally, our
approach is biologically plausible, hardware friendly, and energy-efficient.